Image processing is a process by which an imaging application alters input image data. For example, image processing may be used to change the color space of a digital image. Image processing may be implemented in conjunction with a printing device in order to adjust the color appearance or perceived sharpness of an image according to the specifications of the printing device. Often, an image includes noise artifacts that reduce image quality of the image. In particular, images compressed Joint Photographic Experts Group (JPEG) compression typically suffer from distinct types of deteriorations termed as JPEG artifacts.
Enhancing noisy images typically results in unwanted noise enhancement. The perceptual impact of JPEG artifacts is important for image enhancement, and the extent of the JPEG artifacts may be central to preferred image enhancement for the JPEG image. For example, many typical denoising techniques are based on the extent of JPEG artifacts in the JPEG image. Therefore, it is important to be able to estimate the perceptual impact of the JPEG artifacts prior to image enhancement.
Noise estimation techniques are used to estimate JPEG image artifacts. Currently, there are several methods of JPEG artifact estimation. However, the current available methods each present certain operational drawbacks or limitations. Specifically, typical JPEG artifact estimation techniques are computationally intensive. For example, one technique requires the identification of inbalances of harmonic content of blocks of pixels. This method processes every pixel of the image, resulting in a time consuming and computationally intensive method. Other similar methods for JPEG artifact estimation process the entire image, which is typically inefficient.
Other JPEG estimation methods, such as the use of neural networks, are very labor intensive, and require a significant amount of training. Furthermore, current methods do not account for enlarging or downsampling a JPEG image, which can change the number of perceived JPEG artifacts.